Vernacular Conversational Speech Corpus
Spontaneous multi-speaker conversational audio recorded across 75+ regional dialects with studio Neumann U87 setup.
Quality & Precision Benchmarks
AUDIO SAMPLING
48kHz 24-bit FLAC
DIALECT COUNT
75+ Regional Dialects
NOISE FLOOR
SNR > 48 dB Studio Grade
SLA TURNAROUND
< 24 Hours Express
Dataset Taxonomy & Output Structure
dialect_name (Bhojpuri / Maithili / Marwari / Magahi)
speaker_demographics (Age, Gender, Region District)
audio_sampling_rate (48000 Hz 24-bit FLAC)
transcript_vernacular_script (Devanagari / Native Script)
signal_to_noise_ratio_db (SNR > 48dB)
Specific Type Tasks & Applications
- • Vernacular ASR Speech Recognition Training
- • Conversational AI Voice Agent Alignment
- • Dialect Identification & Classification
What Is Right vs What Is Wrong
| COMMON COMPETITOR ERRORS (WRONG) | BLUE PROJECTS GROUND TRUTH (RIGHT) |
|---|---|
| FAIL: Low-quality 16kHz MP3 recordings with heavy compression artifacts ruining ASR acoustic models | PASS: Studio-grade 48kHz 24-bit uncompressed FLAC audio capturing fine phonetic nuances |
| FAIL: Synthetic read speech lacking spontaneous conversational overlaps and informal dialect phrasing | PASS: Natural multi-speaker conversational dialogues recorded with native dialect speakers |
Files & Audio Example (Python)
import torchaudio
# Load Blue Projects Localized Dialect Audio Type: Vernacular Conversational Speech Corpus
waveform, sr = torchaudio.load("vernacular-conversational-speech-corpus_audio.flac")
print("Audio Sample Rate:", sr, "Hz")
Why Blue Projects for Vernacular Conversational Speech Corpus?
Request a free matched 10-hour 48kHz FLAC sample audio batch in your target regional Indic or global dialect.
Request Free Sample Batch →